The TALA empathic space: Integrating affect and activity recognition into a smart space

Advancement in ambient intelligence is driving the trend towards innovative interaction with computing systems. In this paper, we present our efforts towards the development of the ambient intelligent space TALA, which has the concept of empathy in cognitive science as its architecture's backbo...

Full description

Saved in:
Bibliographic Details
Main Authors: Cu, Jocelynn W., Cabredo, Rafael A., Cu, Gregory G., Inventado, Paul Salvador B., Trogo-Oblena, Rhia S., Suarez, Merlin Teodosia C., Legaspi, Roberto S.
Format: text
Published: Animo Repository 2010
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/1461
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2460/type/native/viewcontent/HUMANCOM.2010.5563342
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-2460
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:faculty_research-24602024-03-02T02:30:33Z The TALA empathic space: Integrating affect and activity recognition into a smart space Cu, Jocelynn W. Cabredo, Rafael A. Cu, Gregory G. Inventado, Paul Salvador B. Trogo-Oblena, Rhia S. Suarez, Merlin Teodosia C. Legaspi, Roberto S. Advancement in ambient intelligence is driving the trend towards innovative interaction with computing systems. In this paper, we present our efforts towards the development of the ambient intelligent space TALA, which has the concept of empathy in cognitive science as its architecture's backbone to guide its human-system interactions. We envision TALA to be capable of automatically identifying its occupant, modeling his/her affective states and activities, and providing empathic responses via changes in ambient settings. We present here the empirical results and analyses we obtained for the first two of this three-fold capability. We constructed face and voice datasets for identity and affect recognition and an activity dataset. Using a multimodal approach, specifically, applying a decision level fusion of independent face and voice models, we obtained accuracies of 88% and 79% for identity and affect recognition, respectively. For activity recognition, classification is 80% accurate even without employing any fusion technique. © 2010 IEEE. 2010-10-28T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1461 info:doi/10.1109/HUMANCOM.2010.5563342 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2460/type/native/viewcontent/HUMANCOM.2010.5563342 Faculty Research Work Animo Repository Emotion recognition Human activity recognition Ambient intelligence Ubiquitous computing Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Emotion recognition
Human activity recognition
Ambient intelligence
Ubiquitous computing
Computer Sciences
spellingShingle Emotion recognition
Human activity recognition
Ambient intelligence
Ubiquitous computing
Computer Sciences
Cu, Jocelynn W.
Cabredo, Rafael A.
Cu, Gregory G.
Inventado, Paul Salvador B.
Trogo-Oblena, Rhia S.
Suarez, Merlin Teodosia C.
Legaspi, Roberto S.
The TALA empathic space: Integrating affect and activity recognition into a smart space
description Advancement in ambient intelligence is driving the trend towards innovative interaction with computing systems. In this paper, we present our efforts towards the development of the ambient intelligent space TALA, which has the concept of empathy in cognitive science as its architecture's backbone to guide its human-system interactions. We envision TALA to be capable of automatically identifying its occupant, modeling his/her affective states and activities, and providing empathic responses via changes in ambient settings. We present here the empirical results and analyses we obtained for the first two of this three-fold capability. We constructed face and voice datasets for identity and affect recognition and an activity dataset. Using a multimodal approach, specifically, applying a decision level fusion of independent face and voice models, we obtained accuracies of 88% and 79% for identity and affect recognition, respectively. For activity recognition, classification is 80% accurate even without employing any fusion technique. © 2010 IEEE.
format text
author Cu, Jocelynn W.
Cabredo, Rafael A.
Cu, Gregory G.
Inventado, Paul Salvador B.
Trogo-Oblena, Rhia S.
Suarez, Merlin Teodosia C.
Legaspi, Roberto S.
author_facet Cu, Jocelynn W.
Cabredo, Rafael A.
Cu, Gregory G.
Inventado, Paul Salvador B.
Trogo-Oblena, Rhia S.
Suarez, Merlin Teodosia C.
Legaspi, Roberto S.
author_sort Cu, Jocelynn W.
title The TALA empathic space: Integrating affect and activity recognition into a smart space
title_short The TALA empathic space: Integrating affect and activity recognition into a smart space
title_full The TALA empathic space: Integrating affect and activity recognition into a smart space
title_fullStr The TALA empathic space: Integrating affect and activity recognition into a smart space
title_full_unstemmed The TALA empathic space: Integrating affect and activity recognition into a smart space
title_sort tala empathic space: integrating affect and activity recognition into a smart space
publisher Animo Repository
publishDate 2010
url https://animorepository.dlsu.edu.ph/faculty_research/1461
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2460/type/native/viewcontent/HUMANCOM.2010.5563342
_version_ 1792664314972209152